Generative AI-Assisted Digital Twin for Power Asset Management
Bibliographic record
Abstract
Power asset management is a critical public safety service to ensure electrical networks' reliability, efficiency, and resilience. Traditional approaches, relying on periodic inspections and reactive maintenance, often leave the grid vulnerable to failures, costly outages, and operational inefficiencies. Adopting digital twin (DT) technology has introduced a paradigm shift by enabling real-time monitoring, predictive maintenance, and network optimization through virtual replicas of physical assets. However, the effectiveness of DTs is highly dependent on the timely availability of high-quality data, which remains a major challenge. In parallel, recent generative artificial intelligence (GenAI) advancements have demonstrated significant potential in data generation, predictive modeling, and decision support. This article proposes a novel framework integrating GenAI with DT technology to enhance power asset management. By leveraging GenAI techniques, such as generative adversarial networks (GANs) for data augmentation, vision-language models (VLMs) for automated asset analysis, and large language models (LLMs) for decision support, the proposed framework aims to improve asset performance monitoring, failure prediction, and maintenance planning. This integration enables a transition toward a proactive and intelligence-driven approach, thus contributing to a more robust, adaptive, and sustainable power infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".